Overview

Dataset statistics

Number of variables86
Number of observations210
Missing cells41
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory164.0 KiB
Average record size in memory799.6 B

Variable types

Numeric6
Categorical80

Alerts

EstateType has constant value ""Constant
DistributionType has constant value ""Constant
Object_price is highly overall correlated with LivingSpace and 10 other fieldsHigh correlation
LivingSpace is highly overall correlated with Object_price and 7 other fieldsHigh correlation
Rooms is highly overall correlated with Object_price and 2 other fieldsHigh correlation
ConstructionYear is highly overall correlated with bidet and 4 other fieldsHigh correlation
abstellraum is highly overall correlated with kable_sat_tvHigh correlation
bidet is highly overall correlated with Object_price and 2 other fieldsHigh correlation
dusche is highly overall correlated with fliesenHigh correlation
elektro is highly overall correlated with Object_price and 4 other fieldsHigh correlation
erdwaerme is highly overall correlated with Object_price and 6 other fieldsHigh correlation
fliesen is highly overall correlated with duscheHigh correlation
frei is highly overall correlated with kunststofffensterHigh correlation
fu\u00dfbodenheizung is highly overall correlated with kontrollierte_be-_und_entl\u00fcftungsanlageHigh correlation
gaestewc is highly overall correlated with LivingSpaceHigh correlation
garage is highly overall correlated with Object_price and 3 other fieldsHigh correlation
kable_sat_tv is highly overall correlated with abstellraum and 1 other fieldsHigh correlation
kamera is highly overall correlated with ConstructionYearHigh correlation
kfw40 is highly overall correlated with ConstructionYearHigh correlation
kontrollierte_be-_und_entl\u00fcftungsanlage is highly overall correlated with Object_price and 4 other fieldsHigh correlation
kunststofffenster is highly overall correlated with freiHigh correlation
luftwp is highly overall correlated with elektro and 1 other fieldsHigh correlation
pantry is highly overall correlated with ConstructionYearHigh correlation
parkett is highly overall correlated with Object_price and 2 other fieldsHigh correlation
reinigung is highly overall correlated with Object_price and 4 other fieldsHigh correlation
rollstuhlgerecht is highly overall correlated with Object_price and 1 other fieldsHigh correlation
speisekammer is highly overall correlated with Object_price and 1 other fieldsHigh correlation
stein is highly overall correlated with ConstructionYearHigh correlation
als_ferienimmobilie_geeignet is highly imbalanced (95.6%)Imbalance
altbau_(bis_1945) is highly imbalanced (64.7%)Imbalance
bad/wc_getrennt is highly imbalanced (78.9%)Imbalance
barriefrei is highly imbalanced (62.9%)Imbalance
bidet is highly imbalanced (95.6%)Imbalance
carport is highly imbalanced (92.2%)Imbalance
dachgeschoss is highly imbalanced (57.8%)Imbalance
dielen is highly imbalanced (92.2%)Imbalance
dsl is highly imbalanced (64.7%)Imbalance
duplex is highly imbalanced (92.2%)Imbalance
elektro is highly imbalanced (68.4%)Imbalance
erdgeschoss is highly imbalanced (59.4%)Imbalance
erdwaerme is highly imbalanced (78.9%)Imbalance
erstbezug is highly imbalanced (72.4%)Imbalance
estrich is highly imbalanced (95.6%)Imbalance
etagenheizung is highly imbalanced (51.6%)Imbalance
fern is highly imbalanced (56.2%)Imbalance
ferne is highly imbalanced (51.6%)Imbalance
garage is highly imbalanced (61.1%)Imbalance
garten is highly imbalanced (59.4%)Imbalance
gartennutzung is highly imbalanced (62.9%)Imbalance
haustiere_erlaubt is highly imbalanced (81.3%)Imbalance
holzfenster is highly imbalanced (62.9%)Imbalance
kamera is highly imbalanced (92.2%)Imbalance
kamin is highly imbalanced (89.2%)Imbalance
kfw40 is highly imbalanced (92.2%)Imbalance
kfw55 is highly imbalanced (95.6%)Imbalance
klimatisiert is highly imbalanced (92.2%)Imbalance
kontrollierte_be-_und_entl\u00fcftungsanlage is highly imbalanced (64.7%)Imbalance
kunststoff is highly imbalanced (78.9%)Imbalance
linoleum is highly imbalanced (83.8%)Imbalance
loggia is highly imbalanced (86.4%)Imbalance
luftwp is highly imbalanced (56.2%)Imbalance
marmor is highly imbalanced (95.6%)Imbalance
massivhaus is highly imbalanced (83.8%)Imbalance
neuwertig is highly imbalanced (83.8%)Imbalance
oel is highly imbalanced (59.4%)Imbalance
ofenheizung is highly imbalanced (89.2%)Imbalance
offene_k\u00fcche is highly imbalanced (50.2%)Imbalance
pantry is highly imbalanced (89.2%)Imbalance
pellet is highly imbalanced (86.4%)Imbalance
reinigung is highly imbalanced (56.2%)Imbalance
rollstuhlgerecht is highly imbalanced (89.2%)Imbalance
sat is highly imbalanced (78.9%)Imbalance
speisekammer is highly imbalanced (86.4%)Imbalance
stein is highly imbalanced (86.4%)Imbalance
teilweise_m\u00f6bliert is highly imbalanced (81.3%)Imbalance
teppich is highly imbalanced (89.2%)Imbalance
tiefgarage is highly imbalanced (61.1%)Imbalance
vermietet is highly imbalanced (89.2%)Imbalance
wasch_trockenraum is highly imbalanced (66.5%)Imbalance
wg_geeignet is highly imbalanced (66.5%)Imbalance
wintergarten is highly imbalanced (86.4%)Imbalance
wohnberechtigungsschein is highly imbalanced (89.2%)Imbalance
ConstructionYear has 41 (19.5%) missing valuesMissing

Reproduction

Analysis started2023-07-12 11:37:12.828687
Analysis finished2023-07-12 11:37:44.530960
Duration31.7 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct177
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.233333
Minimum0
Maximum178
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-07-12T13:37:44.684934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q140.25
median86.5
Q3130.75
95-th percentile168.55
Maximum178
Range178
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation52.279593
Coefficient of variation (CV)0.60625736
Kurtosis-1.2269979
Mean86.233333
Median Absolute Deviation (MAD)45.5
Skewness0.032356472
Sum18109
Variance2733.1558
MonotonicityNot monotonic
2023-07-12T13:37:44.885801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.0%
24 2
 
1.0%
152 2
 
1.0%
36 2
 
1.0%
150 2
 
1.0%
38 2
 
1.0%
147 2
 
1.0%
45 2
 
1.0%
46 2
 
1.0%
48 2
 
1.0%
Other values (167) 190
90.5%
ValueCountFrequency (%)
0 2
1.0%
1 2
1.0%
2 1
0.5%
3 1
0.5%
4 2
1.0%
5 1
0.5%
6 1
0.5%
7 2
1.0%
8 1
0.5%
9 1
0.5%
ValueCountFrequency (%)
178 1
0.5%
176 1
0.5%
175 1
0.5%
174 1
0.5%
173 2
1.0%
172 1
0.5%
171 1
0.5%
170 2
1.0%
169 1
0.5%
168 1
0.5%

Object_price
Real number (ℝ)

HIGH CORRELATION 

Distinct148
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean933.3199
Minimum220
Maximum3601.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-07-12T13:37:45.067442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile334.5
Q1602
median838
Q31100
95-th percentile1776.2405
Maximum3601.44
Range3381.44
Interquartile range (IQR)498

Descriptive statistics

Standard deviation518.30884
Coefficient of variation (CV)0.55533889
Kurtosis6.2690154
Mean933.3199
Median Absolute Deviation (MAD)257.5
Skewness2.052367
Sum195997.18
Variance268644.05
MonotonicityNot monotonic
2023-07-12T13:37:45.232963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750 6
 
2.9%
900 4
 
1.9%
950 4
 
1.9%
1100 4
 
1.9%
1500 4
 
1.9%
680 3
 
1.4%
800 3
 
1.4%
850 3
 
1.4%
695 3
 
1.4%
990 3
 
1.4%
Other values (138) 173
82.4%
ValueCountFrequency (%)
220 2
1.0%
265 1
0.5%
275 1
0.5%
285 1
0.5%
300 1
0.5%
310 1
0.5%
320 1
0.5%
325 1
0.5%
330 2
1.0%
340 2
1.0%
ValueCountFrequency (%)
3601.44 1
0.5%
3163.32 1
0.5%
3030.93 1
0.5%
2880 1
0.5%
2545.6 1
0.5%
2355 1
0.5%
2220 2
1.0%
2175 1
0.5%
1890 1
0.5%
1797.71 1
0.5%

LivingSpace
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.603714
Minimum9.6
Maximum225.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-07-12T13:37:45.415355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.6
5-th percentile19.35
Q148.8925
median68.5
Q388
95-th percentile147.75
Maximum225.09
Range215.49
Interquartile range (IQR)39.1075

Descriptive statistics

Standard deviation38.550801
Coefficient of variation (CV)0.53839108
Kurtosis3.2110351
Mean71.603714
Median Absolute Deviation (MAD)19.5
Skewness1.3465503
Sum15036.78
Variance1486.1643
MonotonicityNot monotonic
2023-07-12T13:37:45.587460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 6
 
2.9%
55 6
 
2.9%
50 5
 
2.4%
65 5
 
2.4%
70 5
 
2.4%
75 5
 
2.4%
72 4
 
1.9%
28 4
 
1.9%
90 4
 
1.9%
79 4
 
1.9%
Other values (120) 162
77.1%
ValueCountFrequency (%)
9.6 1
0.5%
9.8 1
0.5%
10 1
0.5%
11.7 1
0.5%
14.16 2
1.0%
14.38 1
0.5%
15.1 1
0.5%
15.13 1
0.5%
15.26 1
0.5%
18 1
0.5%
ValueCountFrequency (%)
225.09 1
0.5%
218.16 1
0.5%
213 2
1.0%
195 1
0.5%
178.29 1
0.5%
170 1
0.5%
159.1 1
0.5%
153.3 1
0.5%
150 2
1.0%
145 2
1.0%

Rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4238095
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-07-12T13:37:45.727518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4.55
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1008499
Coefficient of variation (CV)0.4541817
Kurtosis0.83783215
Mean2.4238095
Median Absolute Deviation (MAD)1
Skewness0.84182437
Sum509
Variance1.2118706
MonotonicityNot monotonic
2023-07-12T13:37:45.862270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 71
33.8%
3 52
24.8%
1 40
19.0%
4 13
 
6.2%
3.5 9
 
4.3%
2.5 8
 
3.8%
5 7
 
3.3%
1.5 6
 
2.9%
6 3
 
1.4%
5.5 1
 
0.5%
ValueCountFrequency (%)
1 40
19.0%
1.5 6
 
2.9%
2 71
33.8%
2.5 8
 
3.8%
3 52
24.8%
3.5 9
 
4.3%
4 13
 
6.2%
5 7
 
3.3%
5.5 1
 
0.5%
6 3
 
1.4%
ValueCountFrequency (%)
6 3
 
1.4%
5.5 1
 
0.5%
5 7
 
3.3%
4 13
 
6.2%
3.5 9
 
4.3%
3 52
24.8%
2.5 8
 
3.8%
2 71
33.8%
1.5 6
 
2.9%
1 40
19.0%

ConstructionYear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)35.5%
Missing41
Missing (%)19.5%
Infinite0
Infinite (%)0.0%
Mean1986.6391
Minimum1708
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-07-12T13:37:46.023177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1708
5-th percentile1923.6
Q11971
median1990
Q32019
95-th percentile2022.6
Maximum2023
Range315
Interquartile range (IQR)48

Descriptive statistics

Standard deviation37.32714
Coefficient of variation (CV)0.01878909
Kurtosis17.566912
Mean1986.6391
Median Absolute Deviation (MAD)25
Skewness-2.8826742
Sum335742
Variance1393.3154
MonotonicityNot monotonic
2023-07-12T13:37:46.190696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 14
 
6.7%
2019 12
 
5.7%
2023 9
 
4.3%
2015 7
 
3.3%
1970 7
 
3.3%
1974 6
 
2.9%
1998 6
 
2.9%
1980 5
 
2.4%
1985 5
 
2.4%
1993 4
 
1.9%
Other values (50) 94
44.8%
(Missing) 41
19.5%
ValueCountFrequency (%)
1708 1
0.5%
1895 1
0.5%
1900 2
1.0%
1908 1
0.5%
1911 1
0.5%
1915 1
0.5%
1920 2
1.0%
1929 1
0.5%
1930 2
1.0%
1934 1
0.5%
ValueCountFrequency (%)
2023 9
4.3%
2022 14
6.7%
2021 4
 
1.9%
2020 4
 
1.9%
2019 12
5.7%
2018 3
 
1.4%
2017 1
 
0.5%
2016 3
 
1.4%
2015 7
3.3%
2013 1
 
0.5%

ZipCode
Real number (ℝ)

Distinct25
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97127.481
Minimum97070
Maximum97299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-07-12T13:37:46.342357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum97070
5-th percentile97070
Q197074
median97080
Q397215.75
95-th percentile97281.25
Maximum97299
Range229
Interquartile range (IQR)141.75

Descriptive statistics

Standard deviation78.866868
Coefficient of variation (CV)0.00081199334
Kurtosis-0.77932926
Mean97127.481
Median Absolute Deviation (MAD)7
Skewness0.98488395
Sum20396771
Variance6219.9829
MonotonicityNot monotonic
2023-07-12T13:37:46.481034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
97074 36
17.1%
97070 27
12.9%
97082 21
10.0%
97078 16
 
7.6%
97084 13
 
6.2%
97072 13
 
6.2%
97218 12
 
5.7%
97080 11
 
5.2%
97204 8
 
3.8%
97076 8
 
3.8%
Other values (15) 45
21.4%
ValueCountFrequency (%)
97070 27
12.9%
97072 13
 
6.2%
97074 36
17.1%
97076 8
 
3.8%
97078 16
7.6%
97080 11
 
5.2%
97082 21
10.0%
97084 13
 
6.2%
97204 8
 
3.8%
97209 4
 
1.9%
ValueCountFrequency (%)
97299 5
2.4%
97297 5
2.4%
97288 1
 
0.5%
97273 1
 
0.5%
97270 2
 
1.0%
97265 1
 
0.5%
97261 2
 
1.0%
97250 1
 
0.5%
97249 7
3.3%
97246 5
2.4%

EstateType
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
APARTMENT
210 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1890
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPARTMENT
2nd rowAPARTMENT
3rd rowAPARTMENT
4th rowAPARTMENT
5th rowAPARTMENT

Common Values

ValueCountFrequency (%)
APARTMENT 210
100.0%

Length

2023-07-12T13:37:46.619783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:46.777284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
apartment 210
100.0%

Most occurring characters

ValueCountFrequency (%)
A 420
22.2%
T 420
22.2%
P 210
11.1%
R 210
11.1%
M 210
11.1%
E 210
11.1%
N 210
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1890
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 420
22.2%
T 420
22.2%
P 210
11.1%
R 210
11.1%
M 210
11.1%
E 210
11.1%
N 210
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 420
22.2%
T 420
22.2%
P 210
11.1%
R 210
11.1%
M 210
11.1%
E 210
11.1%
N 210
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 420
22.2%
T 420
22.2%
P 210
11.1%
R 210
11.1%
M 210
11.1%
E 210
11.1%
N 210
11.1%

DistributionType
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
RENT
210 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters840
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 210
100.0%

Length

2023-07-12T13:37:46.893656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:47.031332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rent 210
100.0%

Most occurring characters

ValueCountFrequency (%)
R 210
25.0%
E 210
25.0%
N 210
25.0%
T 210
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 840
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 210
25.0%
E 210
25.0%
N 210
25.0%
T 210
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 840
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 210
25.0%
E 210
25.0%
N 210
25.0%
T 210
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 210
25.0%
E 210
25.0%
N 210
25.0%
T 210
25.0%

abstellraum
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
178 
1
32 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 178
84.8%
1 32
 
15.2%

Length

2023-07-12T13:37:47.146416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:47.289286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 178
84.8%
1 32
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 178
84.8%
1 32
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 178
84.8%
1 32
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 178
84.8%
1 32
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 178
84.8%
1 32
 
15.2%

als_ferienimmobilie_geeignet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
209 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Length

2023-07-12T13:37:47.410830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:47.553631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

altbau_(bis_1945)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
196 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Length

2023-07-12T13:37:47.671781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:47.818137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

bad/wc_getrennt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
203 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Length

2023-07-12T13:37:47.941160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:48.089213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

balkon
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
119 
1
91 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Length

2023-07-12T13:37:48.208933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:48.375943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Most occurring characters

ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

barriefrei
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
195 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Length

2023-07-12T13:37:48.506229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:48.664645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

bidet
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
209 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Length

2023-07-12T13:37:48.797362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:48.959573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

carport
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
208 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Length

2023-07-12T13:37:49.085417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:49.238309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

dachgeschoss
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
192 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 192
91.4%
1 18
 
8.6%

Length

2023-07-12T13:37:49.360489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:49.512619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 192
91.4%
1 18
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 192
91.4%
1 18
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 192
91.4%
1 18
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 192
91.4%
1 18
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 192
91.4%
1 18
 
8.6%

dielen
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
208 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Length

2023-07-12T13:37:49.647630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:49.799271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

dsl
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
196 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Length

2023-07-12T13:37:49.925860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:50.071791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

duplex
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
208 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Length

2023-07-12T13:37:50.205959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:50.362413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

dusche
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
122 
1
88 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 122
58.1%
1 88
41.9%

Length

2023-07-12T13:37:50.505889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:50.686930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 122
58.1%
1 88
41.9%

Most occurring characters

ValueCountFrequency (%)
0 122
58.1%
1 88
41.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 122
58.1%
1 88
41.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 122
58.1%
1 88
41.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 122
58.1%
1 88
41.9%

einbauk\u00fcche
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
106 
1
104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 106
50.5%
1 104
49.5%

Length

2023-07-12T13:37:50.822088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:50.989427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106
50.5%
1 104
49.5%

Most occurring characters

ValueCountFrequency (%)
0 106
50.5%
1 104
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
50.5%
1 104
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
50.5%
1 104
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
50.5%
1 104
49.5%

elektro
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
198 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 198
94.3%
1 12
 
5.7%

Length

2023-07-12T13:37:51.129296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:51.267550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 198
94.3%
1 12
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 198
94.3%
1 12
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 198
94.3%
1 12
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 198
94.3%
1 12
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 198
94.3%
1 12
 
5.7%

erdgeschoss
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
193 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Length

2023-07-12T13:37:51.380791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:51.517390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

erdwaerme
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
203 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Length

2023-07-12T13:37:51.633075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:51.772261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

erstbezug
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
200 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 200
95.2%
1 10
 
4.8%

Length

2023-07-12T13:37:52.108815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:52.244853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 200
95.2%
1 10
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 200
95.2%
1 10
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 200
95.2%
1 10
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 200
95.2%
1 10
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 200
95.2%
1 10
 
4.8%

estrich
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
209 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Length

2023-07-12T13:37:52.359748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:52.490871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

etagenheizung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
188 
1
22 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Length

2023-07-12T13:37:52.601002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:52.748422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

fenster
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
119 
1
91 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Length

2023-07-12T13:37:52.933017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:53.120086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Most occurring characters

ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 119
56.7%
1 91
43.3%

fern
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
191 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Length

2023-07-12T13:37:53.250272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:53.399225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

ferne
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
188 
1
22 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Length

2023-07-12T13:37:53.533973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:53.764962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 188
89.5%
1 22
 
10.5%

fliesen
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
136 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136
64.8%
1 74
35.2%

Length

2023-07-12T13:37:53.883244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:54.042601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 136
64.8%
1 74
35.2%

Most occurring characters

ValueCountFrequency (%)
0 136
64.8%
1 74
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136
64.8%
1 74
35.2%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 136
64.8%
1 74
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 136
64.8%
1 74
35.2%

frei
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
174 
1
36 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
82.9%
1 36
 
17.1%

Length

2023-07-12T13:37:54.189557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:54.351509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
82.9%
1 36
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 174
82.9%
1 36
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
82.9%
1 36
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
82.9%
1 36
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
82.9%
1 36
 
17.1%

fu\u00dfbodenheizung
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
170 
1
40 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Length

2023-07-12T13:37:54.477636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:54.637689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

gaestewc
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
170 
1
40 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Length

2023-07-12T13:37:54.783493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:54.956317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
81.0%
1 40
 
19.0%

garage
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
194 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Length

2023-07-12T13:37:55.076443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:55.245100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

garten
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
193 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Length

2023-07-12T13:37:55.387489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:55.546599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

gartennutzung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
195 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Length

2023-07-12T13:37:55.705867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:55.852542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

gas
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
117 
1
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 117
55.7%
1 93
44.3%

Length

2023-07-12T13:37:55.999331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:56.158680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 117
55.7%
1 93
44.3%

Most occurring characters

ValueCountFrequency (%)
0 117
55.7%
1 93
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
55.7%
1 93
44.3%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117
55.7%
1 93
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117
55.7%
1 93
44.3%

gepflegt
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
180 
1
30 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 180
85.7%
1 30
 
14.3%

Length

2023-07-12T13:37:56.326552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:56.491653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 180
85.7%
1 30
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 180
85.7%
1 30
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 180
85.7%
1 30
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 180
85.7%
1 30
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 180
85.7%
1 30
 
14.3%

haustiere_erlaubt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
204 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Length

2023-07-12T13:37:56.630644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:56.775617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

holzfenster
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
195 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Length

2023-07-12T13:37:56.903973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:57.041382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 195
92.9%
1 15
 
7.1%

kable_sat_tv
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
173 
1
37 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
82.4%
1 37
 
17.6%

Length

2023-07-12T13:37:57.164937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:57.299363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
82.4%
1 37
 
17.6%

Most occurring characters

ValueCountFrequency (%)
0 173
82.4%
1 37
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
82.4%
1 37
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
82.4%
1 37
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
82.4%
1 37
 
17.6%

kamera
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
208 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Length

2023-07-12T13:37:57.419113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:57.562711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

kamin
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
207 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Length

2023-07-12T13:37:57.690369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:57.831477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

kelleranteil
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
1
110 
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 110
52.4%
0 100
47.6%

Length

2023-07-12T13:37:57.945714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:58.090461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 110
52.4%
0 100
47.6%

Most occurring characters

ValueCountFrequency (%)
1 110
52.4%
0 100
47.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 110
52.4%
0 100
47.6%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 110
52.4%
0 100
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 110
52.4%
0 100
47.6%

kfw40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
208 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Length

2023-07-12T13:37:58.211755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:58.345853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

kfw55
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
209 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Length

2023-07-12T13:37:58.460770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:58.609323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

klimatisiert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
208 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Length

2023-07-12T13:37:58.723266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:58.863213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 208
99.0%
1 2
 
1.0%

kontrollierte_be-_und_entl\u00fcftungsanlage
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
196 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Length

2023-07-12T13:37:58.981010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:59.117161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 196
93.3%
1 14
 
6.7%

kunststoff
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
203 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Length

2023-07-12T13:37:59.233334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:59.369192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

kunststofffenster
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
165 
1
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 165
78.6%
1 45
 
21.4%

Length

2023-07-12T13:37:59.485024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:59.620227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 165
78.6%
1 45
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 165
78.6%
1 45
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
78.6%
1 45
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 165
78.6%
1 45
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 165
78.6%
1 45
 
21.4%

laminat
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
175 
1
35 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
83.3%
1 35
 
16.7%

Length

2023-07-12T13:37:59.745406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:37:59.901611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
83.3%
1 35
 
16.7%

Most occurring characters

ValueCountFrequency (%)
0 175
83.3%
1 35
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
83.3%
1 35
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
83.3%
1 35
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
83.3%
1 35
 
16.7%

linoleum
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
205 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Length

2023-07-12T13:38:00.035550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:00.199292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

loggia
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
206 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Length

2023-07-12T13:38:00.329437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:00.476126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

luftwp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
191 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Length

2023-07-12T13:38:00.613759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:00.757444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

marmor
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
209 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Length

2023-07-12T13:38:00.880412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:01.050904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
99.5%
1 1
 
0.5%

massivhaus
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
205 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Length

2023-07-12T13:38:01.232727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:01.491577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

moebliert
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
176 
1
34 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
83.8%
1 34
 
16.2%

Length

2023-07-12T13:38:01.608011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:01.748801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
83.8%
1 34
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 176
83.8%
1 34
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
83.8%
1 34
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
83.8%
1 34
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
83.8%
1 34
 
16.2%

neubau
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
179 
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 179
85.2%
1 31
 
14.8%

Length

2023-07-12T13:38:01.865789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:02.025823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 179
85.2%
1 31
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 179
85.2%
1 31
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 179
85.2%
1 31
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 179
85.2%
1 31
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 179
85.2%
1 31
 
14.8%

neuwertig
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
205 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Length

2023-07-12T13:38:02.157398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:02.322599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 205
97.6%
1 5
 
2.4%

oel
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
193 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Length

2023-07-12T13:38:02.474640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:02.644095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 193
91.9%
1 17
 
8.1%

ofenheizung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
207 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Length

2023-07-12T13:38:02.822650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:03.038181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

offene_k\u00fcche
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
187 
1
23 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 187
89.0%
1 23
 
11.0%

Length

2023-07-12T13:38:03.237165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:03.392244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 187
89.0%
1 23
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 187
89.0%
1 23
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 187
89.0%
1 23
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 187
89.0%
1 23
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 187
89.0%
1 23
 
11.0%

pantry
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
207 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Length

2023-07-12T13:38:03.511258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:03.649505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

parkett
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
166 
1
44 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 166
79.0%
1 44
 
21.0%

Length

2023-07-12T13:38:03.762544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:03.912438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 166
79.0%
1 44
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 166
79.0%
1 44
 
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
79.0%
1 44
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
79.0%
1 44
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
79.0%
1 44
 
21.0%

pellet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
206 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Length

2023-07-12T13:38:04.038670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:04.181554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

personenaufzug
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
145 
1
65 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 145
69.0%
1 65
31.0%

Length

2023-07-12T13:38:04.301126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:04.447173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 145
69.0%
1 65
31.0%

Most occurring characters

ValueCountFrequency (%)
0 145
69.0%
1 65
31.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 145
69.0%
1 65
31.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 145
69.0%
1 65
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 145
69.0%
1 65
31.0%

reinigung
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
191 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Length

2023-07-12T13:38:04.570253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:04.710028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 191
91.0%
1 19
 
9.0%

renoviert
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
181 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 181
86.2%
1 29
 
13.8%

Length

2023-07-12T13:38:04.834265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:04.976190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 181
86.2%
1 29
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 181
86.2%
1 29
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181
86.2%
1 29
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181
86.2%
1 29
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181
86.2%
1 29
 
13.8%

rollstuhlgerecht
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
207 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Length

2023-07-12T13:38:05.102270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:05.238310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

sat
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
203 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Length

2023-07-12T13:38:05.353961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:05.494515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 203
96.7%
1 7
 
3.3%

speisekammer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
206 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Length

2023-07-12T13:38:05.613159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:05.753405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

stein
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
206 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Length

2023-07-12T13:38:05.871122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:06.011203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

stellplatz
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
184 
1
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 184
87.6%
1 26
 
12.4%

Length

2023-07-12T13:38:06.132559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:06.279075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 184
87.6%
1 26
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 184
87.6%
1 26
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 184
87.6%
1 26
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 184
87.6%
1 26
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 184
87.6%
1 26
 
12.4%

teilweise_m\u00f6bliert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
204 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Length

2023-07-12T13:38:06.393571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:06.526335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 204
97.1%
1 6
 
2.9%

teppich
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
207 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Length

2023-07-12T13:38:06.882931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:07.023631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

terrasse
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
164 
1
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
78.1%
1 46
 
21.9%

Length

2023-07-12T13:38:07.150540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:07.290566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
78.1%
1 46
 
21.9%

Most occurring characters

ValueCountFrequency (%)
0 164
78.1%
1 46
 
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
78.1%
1 46
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
78.1%
1 46
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
78.1%
1 46
 
21.9%

tiefgarage
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
194 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Length

2023-07-12T13:38:07.413684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:07.557930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 194
92.4%
1 16
 
7.6%

vermietet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
207 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Length

2023-07-12T13:38:07.687511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:07.824027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

wanne
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
116 
1
94 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 116
55.2%
1 94
44.8%

Length

2023-07-12T13:38:07.941875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:08.086141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 116
55.2%
1 94
44.8%

Most occurring characters

ValueCountFrequency (%)
0 116
55.2%
1 94
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 116
55.2%
1 94
44.8%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 116
55.2%
1 94
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 116
55.2%
1 94
44.8%

wasch_trockenraum
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
197 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Length

2023-07-12T13:38:08.209289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:08.356369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

wg_geeignet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
197 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Length

2023-07-12T13:38:08.477938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:08.619003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 197
93.8%
1 13
 
6.2%

wintergarten
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
206 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Length

2023-07-12T13:38:08.740476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:08.879552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206
98.1%
1 4
 
1.9%

wohnberechtigungsschein
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
207 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Length

2023-07-12T13:38:09.000586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:09.140711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 207
98.6%
1 3
 
1.4%

zentralheizung
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
1
113 
0
97 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 113
53.8%
0 97
46.2%

Length

2023-07-12T13:38:09.255860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T13:38:09.394482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 113
53.8%
0 97
46.2%

Most occurring characters

ValueCountFrequency (%)
1 113
53.8%
0 97
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 113
53.8%
0 97
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 113
53.8%
0 97
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 113
53.8%
0 97
46.2%

Interactions

2023-07-12T13:37:42.108835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:38.014196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:38.872688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:39.687883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:40.481078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:41.303401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:42.432564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:38.173057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:39.021652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:39.821929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:40.647576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:41.449510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:42.562760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:38.313028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:39.167330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:39.969975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:40.778973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:41.582489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:42.681625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:38.444027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:39.293581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:40.096449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:40.898694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:41.710543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:42.807317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:38.576170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:39.429439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:40.233689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:41.033446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:41.841932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:42.939436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:38.715726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:39.561187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:40.363540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:41.174869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T13:37:41.980204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-12T13:38:09.697612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
Unnamed: 01.0000.0320.0900.094-0.0030.0270.1330.0000.0000.0000.0000.0000.0000.0000.0650.0000.0410.0000.3090.1690.3200.0930.3380.1410.0710.0000.1940.2040.2070.2620.2080.0420.0000.3070.1450.1080.1810.0000.0000.0000.3050.0000.1690.0000.0220.0000.1780.2740.0000.2140.0000.0000.0000.1190.0000.2110.0000.0770.0350.0850.1260.0000.1290.2630.0000.1110.2200.1370.1670.1420.2640.0710.0000.1250.0000.1710.1870.0000.0600.2120.0000.0770.0000.187
Object_price0.0321.0000.7830.6640.1650.0050.4620.0000.0000.3630.2180.3330.6760.3550.0810.0000.0000.0000.1810.0940.5690.0000.7800.1350.0000.0000.1580.2580.3840.1540.3520.4320.4310.5920.2650.2230.0000.0780.0000.1960.3770.4740.0000.1760.0000.0000.0000.5550.0000.2560.0000.2390.0000.4890.0000.0000.0000.0880.0000.0000.1060.1990.1220.5080.2280.4170.6130.0000.5840.0000.5680.3450.0000.0000.0480.2920.1290.0000.3220.1650.0000.0000.0000.180
LivingSpace0.0900.7831.0000.8540.1350.2450.4620.0000.0000.3890.3470.4330.6760.3110.1720.0000.0000.1210.1010.1120.4310.0000.6120.0000.0000.0000.3090.0830.3580.1070.3310.4040.5610.4500.2980.2390.1570.1470.0000.2480.3710.4730.1630.3250.1210.0000.0000.3930.0000.2170.0000.2940.0000.3940.0000.0000.3480.1240.0000.0590.0000.0000.0000.5090.0000.3050.6100.0000.6410.0000.3750.3350.0000.0000.1310.3050.1390.0000.4070.2260.0000.0000.0000.280
Rooms0.0940.6640.8541.0000.0740.2450.3530.0000.0000.3940.2360.1870.0000.1770.0930.1340.0000.0000.0000.1680.3040.0000.4280.0000.0000.0000.3710.2100.3600.0940.2400.2480.4810.3650.1640.0000.1350.0000.0000.0000.2750.0000.1470.3270.0000.0000.0000.2820.0000.1580.0000.2710.0000.3930.0000.0000.3800.0000.0000.1640.0930.0000.0400.3320.0000.1370.2990.1070.3230.0000.5040.0000.1080.0000.0000.1900.0000.0930.4030.1330.0000.0000.0000.213
ConstructionYear-0.0030.1650.1350.0741.0000.1450.1250.0000.4490.3590.1820.2830.9880.0000.0000.2340.3810.0000.1050.0600.0550.2080.1270.0000.3300.3680.1730.2110.0000.0000.1050.3040.1850.0000.0000.0000.1730.2050.1130.3940.1730.6880.0760.1641.0000.0000.0000.1680.0000.0880.2310.0000.0000.2850.0000.0760.0000.3160.0510.2060.0000.2050.5570.1950.0000.3880.3190.2850.0000.1980.0000.5030.0000.3890.0000.0970.1140.0000.1490.2340.0000.0000.0000.168
ZipCode0.0270.0050.2450.2450.1451.0000.1020.3730.0000.0610.0000.0000.0000.1260.1450.0000.0130.0000.0000.1440.0000.0000.0000.3310.0000.0000.1940.0930.0000.0000.0000.0000.0000.0460.0280.0340.0000.0000.0700.0000.1390.0000.0000.1440.0000.1560.2700.0000.0000.1110.0740.0920.0880.1230.0000.0000.0000.0000.0000.2230.0000.1570.0000.0000.0000.2340.0000.0000.0000.2190.0170.0000.0000.0000.0410.1300.0780.0000.1040.0710.1710.0000.0000.088
abstellraum0.1330.4620.4620.3530.1250.1021.0000.0000.0000.3210.0130.0000.0000.0000.1640.0000.2770.0000.4000.0920.3170.0000.3960.0000.0000.0000.1920.0000.2570.3040.3460.2410.1320.3470.0000.0000.0000.1320.0000.0910.5490.1480.0000.1650.0000.0000.1480.3320.0800.4030.0000.1350.1700.1520.0000.0000.1540.0000.0000.0000.0000.1070.0000.4780.1700.2070.4860.0000.0000.0000.1700.0000.2120.0000.0000.0000.0760.0940.0870.0470.0000.0000.0000.000
als_ferienimmobilie_geeignet0.0000.0000.0000.0000.0000.3730.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
altbau_(bis_1945)0.0000.0000.0000.0000.4490.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.1110.1180.0000.0000.0000.0000.0000.1060.1570.2040.0000.0000.0000.0800.0000.0000.0000.0000.0000.0440.1060.0000.0000.0000.0000.1440.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0000.0600.0480.0000.0000.0000.0000.0000.0000.0000.0310.0000.0520.0000.0000.0000.0000.0180.0000.0000.0960.0000.0000.0000.0000.1970.0000.0000.000
bad/wc_getrennt0.0000.3630.3890.3940.3590.0610.3210.0000.0001.0000.0000.0770.1660.0960.0000.0000.0000.0000.1790.0000.0000.0000.0000.0000.0000.0000.1130.0000.2300.0000.0600.0380.2030.1850.0000.0000.0000.0000.0000.3020.1420.3860.0000.0000.0000.1660.0000.0000.0000.0000.0000.4010.0000.0000.0000.0000.0000.0000.0000.0590.0000.2220.0000.1860.0170.1150.1590.0000.0000.0000.2570.0170.1120.0000.0000.0000.0000.0000.0000.0950.0000.0000.0000.000
balkon0.0000.2180.3470.2360.1820.0000.0130.0000.0000.0001.0000.0880.0000.0000.0000.0000.0710.0000.0000.0000.0000.1950.0370.0000.0000.0000.1410.1250.0000.0560.0000.0350.2900.0620.0740.0000.0000.0000.0850.0000.0000.0000.0000.2170.0000.0000.0000.0630.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.1680.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.2080.0550.0000.2970.0000.0000.0000.0680.000
barriefrei0.0000.3330.4330.1870.2830.0000.0000.0000.0000.0770.0881.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.2590.1630.0000.0650.2080.3060.0000.0530.0000.0000.0000.0000.0000.0000.2500.0000.0690.0000.0920.0000.0000.0000.0000.0710.1200.0000.0250.0000.0000.0680.3740.0000.0000.0000.0000.0000.0000.1490.2660.1200.0000.3500.0000.1490.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.1880.000
bidet0.0000.6760.6760.0000.9880.0000.0000.0000.0000.1660.0000.0921.0000.0000.0000.0000.0980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.3430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0710.0000.0000.0000.0000.2340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
carport0.0000.3550.3110.1770.0000.1260.0000.0000.0000.0960.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.0000.0000.0000.0000.0000.0000.1510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
dachgeschoss0.0650.0810.1720.0930.0000.1450.1640.0000.0000.0000.0000.0000.0000.0001.0000.0000.2150.0000.0750.0540.0000.0000.0000.0000.0760.0570.1070.0000.2470.0890.1380.0000.0000.0210.0000.0000.0990.1780.0000.0410.1320.0000.0000.0000.0000.0000.2230.0000.0000.2240.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.0000.2370.0000.1400.0000.0000.0870.0690.0000.0500.0000.0000.2640.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.084
dielen0.0000.0000.0000.1340.2340.0000.0000.0000.0200.0000.0000.0000.0000.0000.0001.0000.0200.0000.0000.0000.0000.0000.0000.0000.3430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.1510.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
dsl0.0410.0000.0000.0000.3810.0130.2770.0000.0000.0000.0710.0000.0980.0000.2150.0201.0000.0000.1660.0000.0000.1510.0000.0000.0980.2420.0000.0000.0000.0760.1960.0000.0000.0000.0000.0000.1500.0000.0000.2510.3460.0200.0000.0000.0000.0000.2600.0000.2050.2950.0000.0000.1580.0000.0000.1290.0600.1110.0000.0000.0000.0990.0000.2030.1580.0000.3420.0000.0000.2050.0000.1580.0760.2310.0000.0000.2380.0000.0000.0000.0000.0000.0000.000
duplex0.0000.0000.1210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.1370.0000.0000.0000.0000.0000.0000.1520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2720.0000.0000.0000.000
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erdgeschoss0.0930.0000.0000.0000.2080.0000.0000.0000.0000.0000.1950.0000.0000.0000.0000.0000.1510.0000.0000.0000.0001.0000.0000.1200.0000.1390.0000.0000.0700.1080.0250.0000.0000.0000.1880.0530.0000.0000.0000.1390.0920.0000.0000.1050.0000.0000.0000.0660.1750.1490.0000.0000.0000.0980.0000.0000.0820.0000.0000.0000.0000.0000.0000.1060.0000.0000.0980.0000.0000.0000.0000.0000.0000.0810.0000.1900.0390.0000.0000.1640.0000.1340.0000.000
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fenster0.1940.1580.3090.3710.1730.1940.1920.0000.1570.1130.1410.0280.0000.0000.1070.0000.0000.0000.0500.0000.0660.0000.0000.0000.0000.1041.0000.0850.2410.0190.0720.1620.2650.0000.0000.0000.2670.1650.0850.1330.0540.0000.0000.2970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3390.0000.0000.0860.0680.1560.0680.1360.0000.0320.0310.1190.0000.0370.1030.1030.0000.0850.0000.1360.0000.0000.3370.0000.0910.0000.0000.000
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fliesen0.2620.1540.1070.0940.0000.0000.3040.0000.0000.0000.0560.0000.0000.0000.0890.0000.0760.0430.5120.0000.1700.1080.2140.0000.0000.0000.0190.0000.1391.0000.3870.1470.0000.2100.0000.0000.0000.1540.0000.0000.2930.0000.0000.0000.0000.0000.0430.2950.0000.4730.3460.0000.0000.0000.0000.0000.2490.0730.0000.0000.0000.2600.0000.2350.0390.0000.2630.1180.0000.0000.0390.0000.1790.0460.0000.0000.0000.0000.0000.0990.0000.1360.0000.081
frei0.2080.3520.3310.2400.1050.0000.3460.0000.0000.0600.0000.0650.0000.0000.1380.0000.1960.0000.4680.1990.2320.0250.3670.0000.0000.0000.0720.0710.2270.3871.0000.1680.0000.3150.0000.0000.0580.0000.0000.1260.3300.0000.0000.1250.0000.0000.0000.3540.0000.6070.0490.1180.1530.1250.0000.0000.1700.0360.0000.0000.0000.2570.0000.3340.0300.0600.4020.0000.0000.0000.0300.0300.0370.0000.0000.0000.0470.0000.0880.0000.0000.0000.0790.000
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gaestewc0.0000.4310.5610.4810.1850.0000.1320.0000.0000.2030.2900.3060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0400.0000.0000.2650.1000.1990.0000.0000.1341.0000.1420.0000.0350.0000.0000.0000.0000.1240.1220.0000.2720.0000.0000.0000.0560.0000.0000.0140.0000.0000.0000.0000.0000.0700.0560.0000.0000.0000.0450.0000.1690.1380.1740.2830.0000.1850.0000.1380.0000.0000.0000.0650.0410.0000.0000.3250.0000.0000.0000.0000.000
garage0.3070.5920.4500.3650.0000.0460.3470.0000.0000.1850.0620.0000.0000.0000.0210.0000.0000.0000.2380.0530.5060.0000.5940.0000.0000.0000.0000.0000.1510.2100.3150.2870.1421.0000.0000.0000.0000.0900.0000.0000.3080.0000.0000.1310.0000.0000.0000.6040.0000.3470.0000.1120.0000.3730.0000.0000.0750.0640.0000.0000.0000.0000.0000.2630.0000.1190.3090.0000.0000.0000.0000.0000.0420.0000.0000.1590.0000.0000.1800.0000.0000.0000.0000.171
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gartennutzung0.1080.2230.2390.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0780.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.2831.0000.0000.0000.0000.0000.0000.0000.0000.1160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0330.0000.0000.0000.0000.0000.0000.0000.2230.0000.0000.0770.0000.0990.0000.0000.000
gas0.1810.0000.1570.1350.1730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0000.1500.0000.0000.0780.1420.0000.1210.0530.0000.2660.2670.1110.0950.0000.0580.0000.0000.0000.0040.0001.0000.0000.0000.0810.1090.0000.0000.1750.0000.0000.0000.0000.1080.1110.0000.0000.0000.1860.0000.0000.2670.0000.0000.2380.0000.1820.0650.0000.0560.2460.0000.2020.0000.1080.0000.1000.0530.1490.0000.0220.0000.0000.2190.0000.0850.0000.0000.079
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haustiere_erlaubt0.0000.0000.0000.0000.1130.0700.0000.0000.1060.0000.0850.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0850.1830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1150.0000.0000.000
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kable_sat_tv0.3050.3770.3710.2750.1730.1390.5490.0000.0000.1420.0000.0000.0000.0000.1320.0000.3460.0000.3230.0990.2820.0920.3610.0270.0000.1310.0540.0000.2190.2930.3300.1240.1240.3080.0000.0000.1090.1730.0000.2761.0000.0000.0000.1370.0000.0000.1310.1900.0550.3780.0880.0000.0220.1190.0000.2040.0960.0000.0000.0000.0760.0700.0760.3550.0220.1180.5260.0650.0000.0000.0000.0220.0000.1700.0000.0210.1590.0760.1030.2080.0000.0000.0000.000
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laminat0.0000.0000.0000.0000.2310.0740.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.1370.0710.0420.0450.0000.0000.0000.0000.0960.0000.0000.0000.3460.0490.1540.0140.0000.0000.0000.0000.2660.0000.1860.0880.0000.0830.0000.0000.0000.0000.0640.0000.0001.0000.0000.0000.0970.0000.0000.0000.0000.0000.0360.0000.0000.0000.0560.0000.1910.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.0830.0000.1630.0000.1570.0000.000
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marmor0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2750.0000.0000.0000.0000.0000.1660.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
massivhaus0.2110.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0560.0000.0000.0000.0000.0000.0000.0720.0000.0000.0720.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2040.0000.0000.0000.0000.0000.0000.0000.2220.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.028
moebliert0.0000.0000.3480.3800.0000.0000.1540.0000.0600.0000.0000.0680.0000.0000.0880.0000.0600.0000.2330.0990.0410.0820.0000.0000.0000.0000.3390.0930.1090.2490.1700.0000.0700.0750.0820.0000.2670.1030.0000.0680.0960.0000.0000.4170.0000.0000.0000.0600.0000.1340.0000.0000.0000.0930.0000.0001.0000.0000.0000.0820.0000.0000.0000.0000.0000.0460.0930.1410.0000.0000.0000.0000.1280.0000.0000.1390.0000.0000.1890.0000.0000.0000.0000.000
neubau0.0770.0880.1240.0000.3160.0000.0000.0000.0480.0000.0000.3740.0000.0000.0000.0000.1110.1520.0000.0490.0000.0000.0000.0000.0000.0000.0000.0390.0710.0730.0360.2150.0560.0640.0700.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0850.2140.0000.0000.0000.0000.0000.0000.0001.0000.1390.0710.0000.1140.0000.0000.2780.0900.0390.0830.2230.0850.1750.0000.0000.0000.0000.0990.0830.0000.0000.1260.0000.0000.2230.000
neuwertig0.0350.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.1060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.1391.0000.0000.0000.0660.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.1380.0000.0000.0000.000
oel0.0850.0000.0590.1640.2060.2230.0000.0000.0000.0590.1680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0860.0000.0000.0000.0000.0000.0000.0000.0000.0000.2380.1370.0000.0530.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0360.1050.0000.0000.0000.0000.0820.0710.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.1640.0000.1340.0000.136
ofenheizung0.1260.1060.0000.0930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.1390.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0890.0000.0000.0000.0001.0000.0000.4890.0000.0000.0000.0000.0000.0000.0000.0000.4190.0000.0000.0000.0440.1800.0000.0630.0000.0000.0000.0000.000
offene_k\u00fcche0.0000.1990.0000.0000.2050.1570.1070.0520.0000.2220.0000.0000.0520.0000.2370.0000.0990.0000.2980.0000.0000.0000.0000.0000.0520.0650.1560.0000.2440.2600.2570.2680.0450.0000.0000.0000.1820.0670.0000.0000.0700.1890.0000.0000.0000.0520.1890.2330.0000.3120.0000.0000.0000.0480.0000.0000.0000.1140.0660.0000.0001.0000.0000.1620.0000.0000.1090.0960.0000.2220.2200.0960.3950.1540.0000.0000.0730.0000.0000.1120.0000.0000.0000.000
pantry0.1290.1220.0000.0400.5570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0680.0000.1390.0000.0000.0650.0000.0000.0000.0000.0650.0000.0000.0000.0760.0000.0000.0280.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4890.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.4190.0000.0720.0000.0440.1800.0000.0000.0000.0000.0000.0000.000
parkett0.2630.5080.5090.3320.1950.0000.4780.0000.0000.1860.0000.0000.0000.1110.1400.0000.2030.0000.3270.0000.2420.1060.3210.0000.0000.0000.1360.0000.2940.2350.3340.1690.1690.2630.0000.0000.0000.1230.0000.1860.3550.1110.0000.1080.0000.0000.0000.3010.0000.2790.0560.0000.1250.0760.0000.0000.0000.0000.0000.0000.0000.1620.0001.0000.1250.1600.5070.1000.0000.1130.2180.0000.0840.0530.0000.0420.1210.0000.1710.0000.0000.0000.0000.100
pellet0.0000.2280.0000.0000.0000.0000.1700.0000.0000.0170.0000.1490.0000.0000.0000.0000.1580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0300.0000.1380.0000.0000.0000.0560.0000.0000.0000.0220.1510.0000.0700.0000.2340.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.2780.0000.0000.0000.0000.0000.1251.0000.1560.2510.0000.0000.0170.0830.0000.0000.0000.0000.0000.2810.0000.0580.0000.0000.0000.0000.000
personenaufzug0.1110.4170.3050.1370.3880.2340.2070.0000.0310.1150.0000.2660.0000.0000.0000.0000.0000.0000.0000.0680.1030.0000.2390.0000.0000.0350.0320.1510.1790.0000.0600.1740.1740.1190.0000.1050.2460.0000.0000.0000.1180.0630.0000.0890.0000.0000.0000.1110.0660.0000.1910.1130.0000.1900.0000.0000.0460.0900.0150.0000.0000.0000.0000.1600.1561.0000.3390.0260.0000.0000.1560.0650.0000.0000.0000.0430.2850.0000.0000.0000.0000.0000.0000.000
reinigung0.2200.6130.6100.2990.3190.0000.4860.0000.0000.1590.0000.1200.0710.0000.0870.0000.3420.0000.2090.1480.3820.0980.5390.0000.0000.0000.0310.0000.2350.2630.4020.2830.2830.3090.0000.0000.0000.0000.0000.1200.5260.2150.0000.2420.0000.0710.0000.4100.0000.3350.0000.0000.1200.2690.0000.0000.0930.0390.0000.0000.0000.1090.0000.5070.2510.3391.0000.0000.0000.0000.2510.0000.1430.0660.0000.1150.3730.0000.1520.0000.0000.0000.0000.059
renoviert0.1370.0000.0000.1070.2850.0000.0000.0000.0520.0000.0000.0000.0000.0000.0690.0000.0000.0000.1730.1550.0000.0000.0000.0000.0000.0000.1190.0580.0050.1180.0000.0000.0000.0000.0000.0330.2020.1260.1200.0480.0650.0000.0000.0970.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0220.0000.1410.0830.0000.0000.0000.0960.0000.1000.0000.0260.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.041
rollstuhlgerecht0.1670.5840.6410.3230.0000.0000.0000.0000.0000.0000.0680.3500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.1850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.1100.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.000
sat0.1420.0000.0000.0000.1980.2190.0000.0000.0000.0000.0000.0000.0000.0000.0500.0960.2050.0000.0000.0000.0000.0000.0000.0000.1660.0000.0370.0000.0000.0000.0000.0380.0000.0000.0000.0000.1080.0000.0000.0000.0000.0960.0000.0000.0000.1660.0000.2050.0000.1100.0000.0000.2570.0000.0000.0000.0000.0850.0000.0000.0000.2220.0000.1130.0170.0000.0000.0000.0001.0000.0170.0170.2010.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.000
speisekammer0.2640.5680.3750.5040.0000.0170.1700.0000.0000.2570.0000.1490.0000.1510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1030.0000.3440.0390.0300.2330.1380.0000.0000.0000.0000.0000.0000.0000.0000.1510.0000.0000.0000.2340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1750.0000.0000.0000.2200.0000.2180.0830.1560.2510.0000.1100.0171.0000.0000.2010.0000.0000.0000.1410.0000.0000.0000.0000.0000.0000.000
stein0.0710.3450.3350.0000.5030.0000.0000.0000.0000.0170.0000.0000.2340.0000.0000.1510.1580.0000.1090.0000.0000.0000.0000.0000.2340.0000.1030.0000.1020.0000.0300.1380.0000.0000.0000.0000.1000.0000.0000.0000.0220.1510.0000.0000.0000.0000.0000.0000.0000.1210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4190.0960.4190.0000.0000.0650.0000.0000.0000.0170.0001.0000.0000.0420.0000.0000.1410.0000.0980.0000.0000.0000.0000.064
stellplatz0.0000.0000.0000.1080.0000.0000.2120.0000.0180.1120.0000.0000.0000.0000.2640.0000.0760.0000.0800.1150.0000.0000.0000.0000.0000.0000.0000.0630.1120.1790.0370.0640.0000.0420.0000.0000.0530.0260.0000.0610.0000.0000.1190.0000.0000.0380.1740.0000.0000.1600.1020.0000.2010.0630.0380.0000.1280.0000.0000.0260.0000.3950.0000.0840.0000.0000.1430.0000.0000.2010.2010.0001.0000.0000.0000.0000.0460.0000.0000.0000.0000.2010.0000.000
teilweise_m\u00f6bliert0.1250.0000.0000.0000.3890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.1110.2310.0000.0000.0000.0000.0810.0000.0000.1840.1610.0850.0000.0430.0460.0000.0000.0000.0000.0000.0000.1490.2050.0000.0970.1700.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1540.0720.0530.0000.0000.0660.0000.0000.0000.0000.0420.0001.0000.0720.0000.0000.0000.0000.0000.0000.0000.0000.000
teppich0.0000.0480.1310.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0721.0000.0000.0000.0000.0000.0000.0000.0000.0000.017
terrasse0.1710.2920.3050.1900.0970.1300.0000.0000.0960.0000.2080.0710.0000.0000.0000.0000.0000.0000.0000.0000.1250.1900.0000.1650.0000.0000.1360.1300.2810.0000.0000.1860.0410.1590.3650.2230.0220.0000.0000.0000.0210.0000.0000.2300.0000.0000.0000.0000.0000.1110.0000.0000.0000.2870.0000.0000.1390.0990.0000.0000.0440.0000.0440.0420.0000.0430.1150.0000.0440.0000.0000.0000.0000.0000.0001.0000.2960.0000.1180.0000.0890.1180.0000.000
tiefgarage0.1870.1290.1390.0000.1140.0780.0760.0000.0000.0000.0550.0000.0000.0000.0000.0000.2380.0000.0000.0000.0000.0390.0000.2210.0000.0000.0000.0000.2140.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.1590.0000.0000.2090.0000.0860.0000.0000.0000.1650.0000.0000.1410.1080.0000.0000.0000.0830.0000.0000.1800.0730.1800.1210.2810.2850.3730.0000.0000.0000.1410.1410.0460.0000.0000.2961.0000.0000.0000.0000.0000.0000.0000.078
vermietet0.0000.0000.0000.0930.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.1880.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.1100.0000.017
wanne0.0600.3220.4070.4030.1490.1040.0870.0000.0000.0000.2970.0000.0000.0000.0000.0000.0000.0000.0910.0000.1090.0000.1660.1150.0000.0460.3370.0000.1630.0000.0880.2240.3250.1800.0000.0770.2190.0000.0000.0000.1030.0000.0000.3440.0000.0000.0000.1470.0000.0370.0000.0000.0000.0000.0000.0000.1890.0000.0000.0000.0630.0000.0000.1710.0580.0000.1520.0000.0000.0230.0000.0980.0000.0000.0000.1180.0000.0001.0000.0000.1290.0980.0000.000
wasch_trockenraum0.2120.1650.2260.1330.2340.0710.0470.0000.0000.0950.0000.0000.0000.0000.0000.0000.0000.2720.1910.0000.0000.1640.0000.0440.0000.0250.0000.0000.0000.0990.0000.0000.0000.0000.0000.0000.0000.1940.0000.2660.2080.0000.0000.1290.0000.0000.0000.0000.2170.1650.1630.1380.0000.0000.0000.0000.0000.1260.1380.1640.0000.1120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
wg_geeignet0.0000.0000.0000.0000.0000.1710.0000.0000.1970.0000.0000.0000.0000.0000.0000.0000.0000.0000.1430.0430.0000.0000.0000.0000.0000.0000.0910.0600.0000.0000.0000.0000.0000.0000.0000.0990.0850.0000.1150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.1290.0001.0000.0000.0000.000
wintergarten0.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1340.0000.0000.0000.0000.0000.0000.1020.1360.0000.0000.0000.0000.2700.0000.0000.0610.0000.0000.0000.0000.1100.0000.0000.0000.0000.0000.0000.0000.1570.0000.0000.0000.0000.0000.0000.0000.0000.1340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.1180.0000.1100.0980.0000.0001.0000.0000.000
wohnberechtigungsschein0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.1880.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.1580.1390.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.3700.0000.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
zentralheizung0.1870.1800.2800.2130.1680.0880.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.0000.2360.0000.1600.0000.0000.3480.0000.1250.0000.0810.0000.2090.0000.1710.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2610.0000.0000.0000.0900.0000.1420.0000.0280.0000.0000.0000.1360.0000.0000.0000.1000.0000.0000.0590.0410.0000.0000.0000.0640.0000.0000.0170.0000.0780.0170.0000.0000.0000.0000.0001.000

Missing values

2023-07-12T13:37:43.350589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-12T13:37:43.989918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeEstateTypeDistributionTypeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
00995.050.01.52012.097084APARTMENTRENT000010000000110000000001010000000000000000000000001000000000000000000000000000
11475.050.02.0NaN97209APARTMENTRENT000000000000010100010000000000010000010000001000000000000000000000000000110000
22750.070.02.01956.097297APARTMENTRENT000000000000010000001000000000100000000000000001000000000000010000000000101001
33580.074.03.01940.097249APARTMENTRENT001000000000011000001000000010000000010000000000000000000000010000000000001000
44780.086.03.01995.097297APARTMENTRENT000010000000110000001000001000000000010000000000000011000000000000001000100001
55820.094.03.0NaN97297APARTMENTRENT000000000000000000000000001011100000010000000000000000000000000000000000000000
661250.0115.03.52019.097246APARTMENTRENT000011000000000000000100011000000000010000000000000100000001001000000000000000
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88836.079.02.02021.097209APARTMENTRENT100000001010110000000001010000100010000010010000000000010000000000100000000001
991040.072.02.02019.097209APARTMENTRENT100010000000110001001001000000100000000000000000000000000100000000100000100000
Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeEstateTypeDistributionTypeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
200124950.079.003.01911.097072APARTMENTRENT001010000000010000011000000000100000010000000000000000000000000000000000100000
201130750.065.002.51960.097082APARTMENTRENT001000000000010000000000000000100000010000000000000000000000010000000000100001
202138480.041.801.01990.097074APARTMENTRENT000000000010000000010000000000100110010000001000000000000001100000000010100000
2031471290.093.003.52022.097246APARTMENTRENT000001000000010000001000011010000000010000000000000100000011000000000100000000
2041501180.0108.002.01994.097082APARTMENTRENT000000010000110000000000100001100000010000010000000000000100000000000100100001
205152993.672.003.5NaN97236APARTMENTRENT000000000000100000001000100000100000010000000000000000000100000000000000100000
206170695.055.002.01973.097076APARTMENTRENT000010000000010000001000000000000000010000000000000001000001010000000000000001
207173910.096.003.51974.097078APARTMENTRENT000010000000000000001000001000100000010000000000000000000000010000000000100001
2081761200.059.002.01978.097236APARTMENTRENT000010000000010000000001000000000000000000001000001000000000000000000000100000
2091781720.0123.363.02022.097084APARTMENTRENT000011000000010000001010111000100000010000000000000100010101101010100110100001